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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 25 Nov 2011 18:02:48 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/25/t132226219911qk34b7zou4o35.htm/, Retrieved Fri, 02 Jun 2023 09:00:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147364, Retrieved Fri, 02 Jun 2023 09:00:38 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
68897
38683
44720
39525
45315
50380
40600
36279
42438
38064
31879
11379
70249
39253
47060
41697
38708
49267
39018
32228
40870
39383
34571
12066
70938
34077
45409
40809
37013
44953
37848
32745
39401
34931
33008
8620
68906
39556
50669
36432
40891
48428
36222
33425
39401
37967
34801
12657
69116
41519
51321
38529
41547
52073
38401
40898
40439
41888
37898
8771
68184
50530
47221
41756
45633
48138
39486
39341
41117
41629
29722
7054
56676
34870
35117
30169
30936
35699
33228
27733
33666
35429
27438
8170




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147364&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147364&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147364&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Verkoop[t] = + 12478.0714285714 + 57139.9627976191M1[t] + 29412.8363095238M2[t] + 35615.28125M3[t] + 28156.4404761905M4[t] + 29801.3139880952M5[t] + 36841.7589285714M6[t] + 27735.0610119048M7[t] + 24625.6488095238M8[t] + 29635.8080357143M9[t] + 28542.5386904762M10[t] + 22887.412202381M11[t] -55.4449404761905t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Verkoop[t] =  +  12478.0714285714 +  57139.9627976191M1[t] +  29412.8363095238M2[t] +  35615.28125M3[t] +  28156.4404761905M4[t] +  29801.3139880952M5[t] +  36841.7589285714M6[t] +  27735.0610119048M7[t] +  24625.6488095238M8[t] +  29635.8080357143M9[t] +  28542.5386904762M10[t] +  22887.412202381M11[t] -55.4449404761905t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147364&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Verkoop[t] =  +  12478.0714285714 +  57139.9627976191M1[t] +  29412.8363095238M2[t] +  35615.28125M3[t] +  28156.4404761905M4[t] +  29801.3139880952M5[t] +  36841.7589285714M6[t] +  27735.0610119048M7[t] +  24625.6488095238M8[t] +  29635.8080357143M9[t] +  28542.5386904762M10[t] +  22887.412202381M11[t] -55.4449404761905t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147364&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147364&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Verkoop[t] = + 12478.0714285714 + 57139.9627976191M1[t] + 29412.8363095238M2[t] + 35615.28125M3[t] + 28156.4404761905M4[t] + 29801.3139880952M5[t] + 36841.7589285714M6[t] + 27735.0610119048M7[t] + 24625.6488095238M8[t] + 29635.8080357143M9[t] + 28542.5386904762M10[t] + 22887.412202381M11[t] -55.4449404761905t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12478.07142857141741.6702187.164400
M157139.96279761912142.41673326.670800
M229412.83630952382140.80297613.739200
M335615.281252139.3418616.647800
M428156.44047619052138.033713.169300
M529801.31398809522136.87877613.946200
M636841.75892857142135.87733717.24900
M727735.06101190482135.02959812.990500
M824625.64880952382134.33574311.537900
M929635.80803571432133.79592313.888800
M1028542.53869047622133.41025313.378800
M1122887.4122023812133.17881710.729300
t-55.444940476190518.142398-3.05610.0031590.001579

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 12478.0714285714 & 1741.670218 & 7.1644 & 0 & 0 \tabularnewline
M1 & 57139.9627976191 & 2142.416733 & 26.6708 & 0 & 0 \tabularnewline
M2 & 29412.8363095238 & 2140.802976 & 13.7392 & 0 & 0 \tabularnewline
M3 & 35615.28125 & 2139.34186 & 16.6478 & 0 & 0 \tabularnewline
M4 & 28156.4404761905 & 2138.0337 & 13.1693 & 0 & 0 \tabularnewline
M5 & 29801.3139880952 & 2136.878776 & 13.9462 & 0 & 0 \tabularnewline
M6 & 36841.7589285714 & 2135.877337 & 17.249 & 0 & 0 \tabularnewline
M7 & 27735.0610119048 & 2135.029598 & 12.9905 & 0 & 0 \tabularnewline
M8 & 24625.6488095238 & 2134.335743 & 11.5379 & 0 & 0 \tabularnewline
M9 & 29635.8080357143 & 2133.795923 & 13.8888 & 0 & 0 \tabularnewline
M10 & 28542.5386904762 & 2133.410253 & 13.3788 & 0 & 0 \tabularnewline
M11 & 22887.412202381 & 2133.178817 & 10.7293 & 0 & 0 \tabularnewline
t & -55.4449404761905 & 18.142398 & -3.0561 & 0.003159 & 0.001579 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147364&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]12478.0714285714[/C][C]1741.670218[/C][C]7.1644[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]57139.9627976191[/C][C]2142.416733[/C][C]26.6708[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M2[/C][C]29412.8363095238[/C][C]2140.802976[/C][C]13.7392[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]35615.28125[/C][C]2139.34186[/C][C]16.6478[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]28156.4404761905[/C][C]2138.0337[/C][C]13.1693[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]29801.3139880952[/C][C]2136.878776[/C][C]13.9462[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]36841.7589285714[/C][C]2135.877337[/C][C]17.249[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]27735.0610119048[/C][C]2135.029598[/C][C]12.9905[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]24625.6488095238[/C][C]2134.335743[/C][C]11.5379[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]29635.8080357143[/C][C]2133.795923[/C][C]13.8888[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]28542.5386904762[/C][C]2133.410253[/C][C]13.3788[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]22887.412202381[/C][C]2133.178817[/C][C]10.7293[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-55.4449404761905[/C][C]18.142398[/C][C]-3.0561[/C][C]0.003159[/C][C]0.001579[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147364&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147364&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12478.07142857141741.6702187.164400
M157139.96279761912142.41673326.670800
M229412.83630952382140.80297613.739200
M335615.281252139.3418616.647800
M428156.44047619052138.033713.169300
M529801.31398809522136.87877613.946200
M636841.75892857142135.87733717.24900
M727735.06101190482135.02959812.990500
M824625.64880952382134.33574311.537900
M929635.80803571432133.79592313.888800
M1028542.53869047622133.41025313.378800
M1122887.4122023812133.17881710.729300
t-55.444940476190518.142398-3.05610.0031590.001579







Multiple Linear Regression - Regression Statistics
Multiple R0.959244190023146
R-squared0.920149416093161
Adjusted R-squared0.906653542756793
F-TEST (value)68.1800572041263
F-TEST (DF numerator)12
F-TEST (DF denominator)71
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3990.66780371628
Sum Squared Residuals1130705495.89286

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.959244190023146 \tabularnewline
R-squared & 0.920149416093161 \tabularnewline
Adjusted R-squared & 0.906653542756793 \tabularnewline
F-TEST (value) & 68.1800572041263 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 71 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3990.66780371628 \tabularnewline
Sum Squared Residuals & 1130705495.89286 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147364&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.959244190023146[/C][/ROW]
[ROW][C]R-squared[/C][C]0.920149416093161[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.906653542756793[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]68.1800572041263[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]71[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3990.66780371628[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1130705495.89286[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147364&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147364&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.959244190023146
R-squared0.920149416093161
Adjusted R-squared0.906653542756793
F-TEST (value)68.1800572041263
F-TEST (DF numerator)12
F-TEST (DF denominator)71
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3990.66780371628
Sum Squared Residuals1130705495.89286







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16889769562.5892857143-665.589285714273
23868341780.0178571429-3097.01785714286
34472047927.0178571428-3207.01785714285
43952540412.7321428572-887.732142857155
54531542002.16071428573312.83928571427
65038048987.16071428571392.8392857143
74060039825.0178571429774.982142857135
83627936660.1607142857-381.160714285716
94243841614.875823.125
103806440466.1607142857-2402.16071428572
113187934755.5892857143-2876.58928571429
121137911812.7321428571-433.73214285714
137024968897.251351.75
143925341114.6785714286-1861.67857142857
154706047261.6785714286-201.678571428572
164169739747.39285714291949.60714285715
173870841336.8214285714-2628.82142857142
184926748321.8214285714945.178571428568
193901839159.6785714286-141.678571428569
203222835994.8214285714-3766.82142857143
214087040949.5357142857-79.5357142857137
223938339800.8214285714-417.821428571427
233457134090.25480.750000000002
241206611147.3928571429918.607142857143
257093868231.91071428572706.08928571428
263407740449.3392857143-6372.33928571428
274540946596.3392857143-1187.33928571429
284080939082.05357142861726.94642857143
293701340671.4821428571-3658.48214285714
304495347656.4821428571-2703.48214285714
313784838494.3392857143-646.339285714284
323274535329.4821428571-2584.48214285714
333940140284.1964285714-883.196428571427
343493139135.4821428571-4204.48214285714
353300833424.9107142857-416.910714285713
36862010482.0535714286-1862.05357142857
376890667566.57142857141339.42857142857
383955639784-227.999999999999
3950669459314738
403643238416.7142857143-1984.71428571428
414089140006.1428571429884.857142857146
424842846991.14285714291436.85714285714
433622237829-1607
443342534664.1428571429-1239.14285714286
453940139618.8571428571-217.857142857142
463796738470.1428571429-503.142857142856
473480132759.57142857142041.42857142857
48126579816.714285714292840.28571428571
496911666901.23214285712214.76785714285
504151939118.66071428572400.33928571429
515132145265.66071428576055.33928571428
523852937751.375777.625000000004
534154739340.80357142862206.19642857143
545207346325.80357142865747.19642857142
553840137163.66071428571237.33928571429
564089833998.80357142866899.19642857143
574043938953.51785714291485.48214285714
584188837804.80357142864083.19642857143
593789832094.23214285715803.76785714286
6087719151.375-380.375
616818466235.89285714291948.10714285714
625053038453.321428571412076.6785714286
634722144600.32142857142620.67857142857
644175637086.03571428574669.96428571429
654563338675.46428571436957.53571428571
664813845660.46428571432477.53571428571
673948636498.32142857142987.67857142857
683934133333.46428571436007.53571428571
694111738288.17857142862828.82142857143
704162937139.46428571434489.53571428571
712972231428.8928571429-1706.89285714286
7270548486.03571428572-1432.03571428572
735667665570.5535714286-8894.55357142857
743487037787.9821428571-2917.98214285714
753511743934.9821428571-8817.98214285714
763016936420.6964285714-6251.69642857143
773093638010.125-7074.125
783569944995.125-9296.125
793322835832.9821428571-2604.98214285714
802773332668.125-4935.125
813366637622.8392857143-3956.83928571429
823542936474.125-1045.125
832743830763.5535714286-3325.55357142857
8481707820.69642857143349.303571428571

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 68897 & 69562.5892857143 & -665.589285714273 \tabularnewline
2 & 38683 & 41780.0178571429 & -3097.01785714286 \tabularnewline
3 & 44720 & 47927.0178571428 & -3207.01785714285 \tabularnewline
4 & 39525 & 40412.7321428572 & -887.732142857155 \tabularnewline
5 & 45315 & 42002.1607142857 & 3312.83928571427 \tabularnewline
6 & 50380 & 48987.1607142857 & 1392.8392857143 \tabularnewline
7 & 40600 & 39825.0178571429 & 774.982142857135 \tabularnewline
8 & 36279 & 36660.1607142857 & -381.160714285716 \tabularnewline
9 & 42438 & 41614.875 & 823.125 \tabularnewline
10 & 38064 & 40466.1607142857 & -2402.16071428572 \tabularnewline
11 & 31879 & 34755.5892857143 & -2876.58928571429 \tabularnewline
12 & 11379 & 11812.7321428571 & -433.73214285714 \tabularnewline
13 & 70249 & 68897.25 & 1351.75 \tabularnewline
14 & 39253 & 41114.6785714286 & -1861.67857142857 \tabularnewline
15 & 47060 & 47261.6785714286 & -201.678571428572 \tabularnewline
16 & 41697 & 39747.3928571429 & 1949.60714285715 \tabularnewline
17 & 38708 & 41336.8214285714 & -2628.82142857142 \tabularnewline
18 & 49267 & 48321.8214285714 & 945.178571428568 \tabularnewline
19 & 39018 & 39159.6785714286 & -141.678571428569 \tabularnewline
20 & 32228 & 35994.8214285714 & -3766.82142857143 \tabularnewline
21 & 40870 & 40949.5357142857 & -79.5357142857137 \tabularnewline
22 & 39383 & 39800.8214285714 & -417.821428571427 \tabularnewline
23 & 34571 & 34090.25 & 480.750000000002 \tabularnewline
24 & 12066 & 11147.3928571429 & 918.607142857143 \tabularnewline
25 & 70938 & 68231.9107142857 & 2706.08928571428 \tabularnewline
26 & 34077 & 40449.3392857143 & -6372.33928571428 \tabularnewline
27 & 45409 & 46596.3392857143 & -1187.33928571429 \tabularnewline
28 & 40809 & 39082.0535714286 & 1726.94642857143 \tabularnewline
29 & 37013 & 40671.4821428571 & -3658.48214285714 \tabularnewline
30 & 44953 & 47656.4821428571 & -2703.48214285714 \tabularnewline
31 & 37848 & 38494.3392857143 & -646.339285714284 \tabularnewline
32 & 32745 & 35329.4821428571 & -2584.48214285714 \tabularnewline
33 & 39401 & 40284.1964285714 & -883.196428571427 \tabularnewline
34 & 34931 & 39135.4821428571 & -4204.48214285714 \tabularnewline
35 & 33008 & 33424.9107142857 & -416.910714285713 \tabularnewline
36 & 8620 & 10482.0535714286 & -1862.05357142857 \tabularnewline
37 & 68906 & 67566.5714285714 & 1339.42857142857 \tabularnewline
38 & 39556 & 39784 & -227.999999999999 \tabularnewline
39 & 50669 & 45931 & 4738 \tabularnewline
40 & 36432 & 38416.7142857143 & -1984.71428571428 \tabularnewline
41 & 40891 & 40006.1428571429 & 884.857142857146 \tabularnewline
42 & 48428 & 46991.1428571429 & 1436.85714285714 \tabularnewline
43 & 36222 & 37829 & -1607 \tabularnewline
44 & 33425 & 34664.1428571429 & -1239.14285714286 \tabularnewline
45 & 39401 & 39618.8571428571 & -217.857142857142 \tabularnewline
46 & 37967 & 38470.1428571429 & -503.142857142856 \tabularnewline
47 & 34801 & 32759.5714285714 & 2041.42857142857 \tabularnewline
48 & 12657 & 9816.71428571429 & 2840.28571428571 \tabularnewline
49 & 69116 & 66901.2321428571 & 2214.76785714285 \tabularnewline
50 & 41519 & 39118.6607142857 & 2400.33928571429 \tabularnewline
51 & 51321 & 45265.6607142857 & 6055.33928571428 \tabularnewline
52 & 38529 & 37751.375 & 777.625000000004 \tabularnewline
53 & 41547 & 39340.8035714286 & 2206.19642857143 \tabularnewline
54 & 52073 & 46325.8035714286 & 5747.19642857142 \tabularnewline
55 & 38401 & 37163.6607142857 & 1237.33928571429 \tabularnewline
56 & 40898 & 33998.8035714286 & 6899.19642857143 \tabularnewline
57 & 40439 & 38953.5178571429 & 1485.48214285714 \tabularnewline
58 & 41888 & 37804.8035714286 & 4083.19642857143 \tabularnewline
59 & 37898 & 32094.2321428571 & 5803.76785714286 \tabularnewline
60 & 8771 & 9151.375 & -380.375 \tabularnewline
61 & 68184 & 66235.8928571429 & 1948.10714285714 \tabularnewline
62 & 50530 & 38453.3214285714 & 12076.6785714286 \tabularnewline
63 & 47221 & 44600.3214285714 & 2620.67857142857 \tabularnewline
64 & 41756 & 37086.0357142857 & 4669.96428571429 \tabularnewline
65 & 45633 & 38675.4642857143 & 6957.53571428571 \tabularnewline
66 & 48138 & 45660.4642857143 & 2477.53571428571 \tabularnewline
67 & 39486 & 36498.3214285714 & 2987.67857142857 \tabularnewline
68 & 39341 & 33333.4642857143 & 6007.53571428571 \tabularnewline
69 & 41117 & 38288.1785714286 & 2828.82142857143 \tabularnewline
70 & 41629 & 37139.4642857143 & 4489.53571428571 \tabularnewline
71 & 29722 & 31428.8928571429 & -1706.89285714286 \tabularnewline
72 & 7054 & 8486.03571428572 & -1432.03571428572 \tabularnewline
73 & 56676 & 65570.5535714286 & -8894.55357142857 \tabularnewline
74 & 34870 & 37787.9821428571 & -2917.98214285714 \tabularnewline
75 & 35117 & 43934.9821428571 & -8817.98214285714 \tabularnewline
76 & 30169 & 36420.6964285714 & -6251.69642857143 \tabularnewline
77 & 30936 & 38010.125 & -7074.125 \tabularnewline
78 & 35699 & 44995.125 & -9296.125 \tabularnewline
79 & 33228 & 35832.9821428571 & -2604.98214285714 \tabularnewline
80 & 27733 & 32668.125 & -4935.125 \tabularnewline
81 & 33666 & 37622.8392857143 & -3956.83928571429 \tabularnewline
82 & 35429 & 36474.125 & -1045.125 \tabularnewline
83 & 27438 & 30763.5535714286 & -3325.55357142857 \tabularnewline
84 & 8170 & 7820.69642857143 & 349.303571428571 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147364&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]68897[/C][C]69562.5892857143[/C][C]-665.589285714273[/C][/ROW]
[ROW][C]2[/C][C]38683[/C][C]41780.0178571429[/C][C]-3097.01785714286[/C][/ROW]
[ROW][C]3[/C][C]44720[/C][C]47927.0178571428[/C][C]-3207.01785714285[/C][/ROW]
[ROW][C]4[/C][C]39525[/C][C]40412.7321428572[/C][C]-887.732142857155[/C][/ROW]
[ROW][C]5[/C][C]45315[/C][C]42002.1607142857[/C][C]3312.83928571427[/C][/ROW]
[ROW][C]6[/C][C]50380[/C][C]48987.1607142857[/C][C]1392.8392857143[/C][/ROW]
[ROW][C]7[/C][C]40600[/C][C]39825.0178571429[/C][C]774.982142857135[/C][/ROW]
[ROW][C]8[/C][C]36279[/C][C]36660.1607142857[/C][C]-381.160714285716[/C][/ROW]
[ROW][C]9[/C][C]42438[/C][C]41614.875[/C][C]823.125[/C][/ROW]
[ROW][C]10[/C][C]38064[/C][C]40466.1607142857[/C][C]-2402.16071428572[/C][/ROW]
[ROW][C]11[/C][C]31879[/C][C]34755.5892857143[/C][C]-2876.58928571429[/C][/ROW]
[ROW][C]12[/C][C]11379[/C][C]11812.7321428571[/C][C]-433.73214285714[/C][/ROW]
[ROW][C]13[/C][C]70249[/C][C]68897.25[/C][C]1351.75[/C][/ROW]
[ROW][C]14[/C][C]39253[/C][C]41114.6785714286[/C][C]-1861.67857142857[/C][/ROW]
[ROW][C]15[/C][C]47060[/C][C]47261.6785714286[/C][C]-201.678571428572[/C][/ROW]
[ROW][C]16[/C][C]41697[/C][C]39747.3928571429[/C][C]1949.60714285715[/C][/ROW]
[ROW][C]17[/C][C]38708[/C][C]41336.8214285714[/C][C]-2628.82142857142[/C][/ROW]
[ROW][C]18[/C][C]49267[/C][C]48321.8214285714[/C][C]945.178571428568[/C][/ROW]
[ROW][C]19[/C][C]39018[/C][C]39159.6785714286[/C][C]-141.678571428569[/C][/ROW]
[ROW][C]20[/C][C]32228[/C][C]35994.8214285714[/C][C]-3766.82142857143[/C][/ROW]
[ROW][C]21[/C][C]40870[/C][C]40949.5357142857[/C][C]-79.5357142857137[/C][/ROW]
[ROW][C]22[/C][C]39383[/C][C]39800.8214285714[/C][C]-417.821428571427[/C][/ROW]
[ROW][C]23[/C][C]34571[/C][C]34090.25[/C][C]480.750000000002[/C][/ROW]
[ROW][C]24[/C][C]12066[/C][C]11147.3928571429[/C][C]918.607142857143[/C][/ROW]
[ROW][C]25[/C][C]70938[/C][C]68231.9107142857[/C][C]2706.08928571428[/C][/ROW]
[ROW][C]26[/C][C]34077[/C][C]40449.3392857143[/C][C]-6372.33928571428[/C][/ROW]
[ROW][C]27[/C][C]45409[/C][C]46596.3392857143[/C][C]-1187.33928571429[/C][/ROW]
[ROW][C]28[/C][C]40809[/C][C]39082.0535714286[/C][C]1726.94642857143[/C][/ROW]
[ROW][C]29[/C][C]37013[/C][C]40671.4821428571[/C][C]-3658.48214285714[/C][/ROW]
[ROW][C]30[/C][C]44953[/C][C]47656.4821428571[/C][C]-2703.48214285714[/C][/ROW]
[ROW][C]31[/C][C]37848[/C][C]38494.3392857143[/C][C]-646.339285714284[/C][/ROW]
[ROW][C]32[/C][C]32745[/C][C]35329.4821428571[/C][C]-2584.48214285714[/C][/ROW]
[ROW][C]33[/C][C]39401[/C][C]40284.1964285714[/C][C]-883.196428571427[/C][/ROW]
[ROW][C]34[/C][C]34931[/C][C]39135.4821428571[/C][C]-4204.48214285714[/C][/ROW]
[ROW][C]35[/C][C]33008[/C][C]33424.9107142857[/C][C]-416.910714285713[/C][/ROW]
[ROW][C]36[/C][C]8620[/C][C]10482.0535714286[/C][C]-1862.05357142857[/C][/ROW]
[ROW][C]37[/C][C]68906[/C][C]67566.5714285714[/C][C]1339.42857142857[/C][/ROW]
[ROW][C]38[/C][C]39556[/C][C]39784[/C][C]-227.999999999999[/C][/ROW]
[ROW][C]39[/C][C]50669[/C][C]45931[/C][C]4738[/C][/ROW]
[ROW][C]40[/C][C]36432[/C][C]38416.7142857143[/C][C]-1984.71428571428[/C][/ROW]
[ROW][C]41[/C][C]40891[/C][C]40006.1428571429[/C][C]884.857142857146[/C][/ROW]
[ROW][C]42[/C][C]48428[/C][C]46991.1428571429[/C][C]1436.85714285714[/C][/ROW]
[ROW][C]43[/C][C]36222[/C][C]37829[/C][C]-1607[/C][/ROW]
[ROW][C]44[/C][C]33425[/C][C]34664.1428571429[/C][C]-1239.14285714286[/C][/ROW]
[ROW][C]45[/C][C]39401[/C][C]39618.8571428571[/C][C]-217.857142857142[/C][/ROW]
[ROW][C]46[/C][C]37967[/C][C]38470.1428571429[/C][C]-503.142857142856[/C][/ROW]
[ROW][C]47[/C][C]34801[/C][C]32759.5714285714[/C][C]2041.42857142857[/C][/ROW]
[ROW][C]48[/C][C]12657[/C][C]9816.71428571429[/C][C]2840.28571428571[/C][/ROW]
[ROW][C]49[/C][C]69116[/C][C]66901.2321428571[/C][C]2214.76785714285[/C][/ROW]
[ROW][C]50[/C][C]41519[/C][C]39118.6607142857[/C][C]2400.33928571429[/C][/ROW]
[ROW][C]51[/C][C]51321[/C][C]45265.6607142857[/C][C]6055.33928571428[/C][/ROW]
[ROW][C]52[/C][C]38529[/C][C]37751.375[/C][C]777.625000000004[/C][/ROW]
[ROW][C]53[/C][C]41547[/C][C]39340.8035714286[/C][C]2206.19642857143[/C][/ROW]
[ROW][C]54[/C][C]52073[/C][C]46325.8035714286[/C][C]5747.19642857142[/C][/ROW]
[ROW][C]55[/C][C]38401[/C][C]37163.6607142857[/C][C]1237.33928571429[/C][/ROW]
[ROW][C]56[/C][C]40898[/C][C]33998.8035714286[/C][C]6899.19642857143[/C][/ROW]
[ROW][C]57[/C][C]40439[/C][C]38953.5178571429[/C][C]1485.48214285714[/C][/ROW]
[ROW][C]58[/C][C]41888[/C][C]37804.8035714286[/C][C]4083.19642857143[/C][/ROW]
[ROW][C]59[/C][C]37898[/C][C]32094.2321428571[/C][C]5803.76785714286[/C][/ROW]
[ROW][C]60[/C][C]8771[/C][C]9151.375[/C][C]-380.375[/C][/ROW]
[ROW][C]61[/C][C]68184[/C][C]66235.8928571429[/C][C]1948.10714285714[/C][/ROW]
[ROW][C]62[/C][C]50530[/C][C]38453.3214285714[/C][C]12076.6785714286[/C][/ROW]
[ROW][C]63[/C][C]47221[/C][C]44600.3214285714[/C][C]2620.67857142857[/C][/ROW]
[ROW][C]64[/C][C]41756[/C][C]37086.0357142857[/C][C]4669.96428571429[/C][/ROW]
[ROW][C]65[/C][C]45633[/C][C]38675.4642857143[/C][C]6957.53571428571[/C][/ROW]
[ROW][C]66[/C][C]48138[/C][C]45660.4642857143[/C][C]2477.53571428571[/C][/ROW]
[ROW][C]67[/C][C]39486[/C][C]36498.3214285714[/C][C]2987.67857142857[/C][/ROW]
[ROW][C]68[/C][C]39341[/C][C]33333.4642857143[/C][C]6007.53571428571[/C][/ROW]
[ROW][C]69[/C][C]41117[/C][C]38288.1785714286[/C][C]2828.82142857143[/C][/ROW]
[ROW][C]70[/C][C]41629[/C][C]37139.4642857143[/C][C]4489.53571428571[/C][/ROW]
[ROW][C]71[/C][C]29722[/C][C]31428.8928571429[/C][C]-1706.89285714286[/C][/ROW]
[ROW][C]72[/C][C]7054[/C][C]8486.03571428572[/C][C]-1432.03571428572[/C][/ROW]
[ROW][C]73[/C][C]56676[/C][C]65570.5535714286[/C][C]-8894.55357142857[/C][/ROW]
[ROW][C]74[/C][C]34870[/C][C]37787.9821428571[/C][C]-2917.98214285714[/C][/ROW]
[ROW][C]75[/C][C]35117[/C][C]43934.9821428571[/C][C]-8817.98214285714[/C][/ROW]
[ROW][C]76[/C][C]30169[/C][C]36420.6964285714[/C][C]-6251.69642857143[/C][/ROW]
[ROW][C]77[/C][C]30936[/C][C]38010.125[/C][C]-7074.125[/C][/ROW]
[ROW][C]78[/C][C]35699[/C][C]44995.125[/C][C]-9296.125[/C][/ROW]
[ROW][C]79[/C][C]33228[/C][C]35832.9821428571[/C][C]-2604.98214285714[/C][/ROW]
[ROW][C]80[/C][C]27733[/C][C]32668.125[/C][C]-4935.125[/C][/ROW]
[ROW][C]81[/C][C]33666[/C][C]37622.8392857143[/C][C]-3956.83928571429[/C][/ROW]
[ROW][C]82[/C][C]35429[/C][C]36474.125[/C][C]-1045.125[/C][/ROW]
[ROW][C]83[/C][C]27438[/C][C]30763.5535714286[/C][C]-3325.55357142857[/C][/ROW]
[ROW][C]84[/C][C]8170[/C][C]7820.69642857143[/C][C]349.303571428571[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147364&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147364&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16889769562.5892857143-665.589285714273
23868341780.0178571429-3097.01785714286
34472047927.0178571428-3207.01785714285
43952540412.7321428572-887.732142857155
54531542002.16071428573312.83928571427
65038048987.16071428571392.8392857143
74060039825.0178571429774.982142857135
83627936660.1607142857-381.160714285716
94243841614.875823.125
103806440466.1607142857-2402.16071428572
113187934755.5892857143-2876.58928571429
121137911812.7321428571-433.73214285714
137024968897.251351.75
143925341114.6785714286-1861.67857142857
154706047261.6785714286-201.678571428572
164169739747.39285714291949.60714285715
173870841336.8214285714-2628.82142857142
184926748321.8214285714945.178571428568
193901839159.6785714286-141.678571428569
203222835994.8214285714-3766.82142857143
214087040949.5357142857-79.5357142857137
223938339800.8214285714-417.821428571427
233457134090.25480.750000000002
241206611147.3928571429918.607142857143
257093868231.91071428572706.08928571428
263407740449.3392857143-6372.33928571428
274540946596.3392857143-1187.33928571429
284080939082.05357142861726.94642857143
293701340671.4821428571-3658.48214285714
304495347656.4821428571-2703.48214285714
313784838494.3392857143-646.339285714284
323274535329.4821428571-2584.48214285714
333940140284.1964285714-883.196428571427
343493139135.4821428571-4204.48214285714
353300833424.9107142857-416.910714285713
36862010482.0535714286-1862.05357142857
376890667566.57142857141339.42857142857
383955639784-227.999999999999
3950669459314738
403643238416.7142857143-1984.71428571428
414089140006.1428571429884.857142857146
424842846991.14285714291436.85714285714
433622237829-1607
443342534664.1428571429-1239.14285714286
453940139618.8571428571-217.857142857142
463796738470.1428571429-503.142857142856
473480132759.57142857142041.42857142857
48126579816.714285714292840.28571428571
496911666901.23214285712214.76785714285
504151939118.66071428572400.33928571429
515132145265.66071428576055.33928571428
523852937751.375777.625000000004
534154739340.80357142862206.19642857143
545207346325.80357142865747.19642857142
553840137163.66071428571237.33928571429
564089833998.80357142866899.19642857143
574043938953.51785714291485.48214285714
584188837804.80357142864083.19642857143
593789832094.23214285715803.76785714286
6087719151.375-380.375
616818466235.89285714291948.10714285714
625053038453.321428571412076.6785714286
634722144600.32142857142620.67857142857
644175637086.03571428574669.96428571429
654563338675.46428571436957.53571428571
664813845660.46428571432477.53571428571
673948636498.32142857142987.67857142857
683934133333.46428571436007.53571428571
694111738288.17857142862828.82142857143
704162937139.46428571434489.53571428571
712972231428.8928571429-1706.89285714286
7270548486.03571428572-1432.03571428572
735667665570.5535714286-8894.55357142857
743487037787.9821428571-2917.98214285714
753511743934.9821428571-8817.98214285714
763016936420.6964285714-6251.69642857143
773093638010.125-7074.125
783569944995.125-9296.125
793322835832.9821428571-2604.98214285714
802773332668.125-4935.125
813366637622.8392857143-3956.83928571429
823542936474.125-1045.125
832743830763.5535714286-3325.55357142857
8481707820.69642857143349.303571428571







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.00314678032168490.00629356064336980.996853219678315
170.1644853569517930.3289707139035870.835514643048207
180.0825483590844440.1650967181688880.917451640915556
190.03974873517355840.07949747034711670.960251264826442
200.02997916215861440.05995832431722890.970020837841386
210.01335338698334970.02670677396669940.98664661301665
220.007014565818463780.01402913163692760.992985434181536
230.004840564077165480.009681128154330970.995159435922835
240.002077850440182360.004155700880364720.997922149559818
250.001016844873303640.002033689746607290.998983155126696
260.002216655624163310.004433311248326620.997783344375837
270.001012164830874230.002024329661748450.998987835169126
280.0004452583459652950.0008905166919305890.999554741654035
290.0006254207468970830.001250841493794170.999374579253103
300.0006087405109505940.001217481021901190.999391259489049
310.0002744310091705360.0005488620183410720.999725568990829
320.0001514815985854610.0003029631971709220.999848518401415
336.91038568810129e-050.0001382077137620260.999930896143119
346.96353220422635e-050.0001392706440845270.999930364677958
353.94892344527214e-057.89784689054428e-050.999960510765547
362.53073627633501e-055.06147255267003e-050.999974692637237
371.11827926846848e-052.23655853693695e-050.999988817207315
383.01734527774741e-056.03469055549482e-050.999969826547223
390.0001550072465478710.0003100144930957420.999844992753452
400.0001487577887472760.0002975155774945520.999851242211253
419.56311511331242e-050.0001912623022662480.999904368848867
425.12898003509668e-050.0001025796007019340.999948710199649
434.53062056899762e-059.06124113799524e-050.99995469379431
446.20522679736367e-050.0001241045359472730.999937947732026
454.83152214827383e-059.66304429654767e-050.999951684778517
460.0001079920620254760.0002159841240509520.999892007937975
470.0001239747323074760.0002479494646149520.999876025267692
480.0001273097475640960.0002546194951281920.999872690252436
496.01245443029656e-050.0001202490886059310.999939875455697
500.0002784979869877040.0005569959739754070.999721502013012
510.0004232210692009440.0008464421384018870.999576778930799
520.0003497203842271330.0006994407684542650.999650279615773
530.000296690235752370.000593380471504740.999703309764248
540.00029137773867220.0005827554773444010.999708622261328
550.0003416710344439560.0006833420688879110.999658328965556
560.0009410651388442690.001882130277688540.999058934861156
570.001140516574927550.002281033149855110.998859483425072
580.00271160891033810.005423217820676210.997288391089662
590.002145867942111330.004291735884222670.997854132057889
600.03311773860790240.06623547721580480.966882261392098
610.02330985234734710.04661970469469410.976690147652653
620.1371694644507420.2743389289014840.862830535549258
630.1176277281248110.2352554562496220.882372271875189
640.09716054637908530.1943210927581710.902839453620915
650.1983264722980450.3966529445960890.801673527701956
660.2938833511012080.5877667022024160.706116648898792
670.1890495766423980.3780991532847960.810950423357602
680.3938026903838030.7876053807676060.606197309616197

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0031467803216849 & 0.0062935606433698 & 0.996853219678315 \tabularnewline
17 & 0.164485356951793 & 0.328970713903587 & 0.835514643048207 \tabularnewline
18 & 0.082548359084444 & 0.165096718168888 & 0.917451640915556 \tabularnewline
19 & 0.0397487351735584 & 0.0794974703471167 & 0.960251264826442 \tabularnewline
20 & 0.0299791621586144 & 0.0599583243172289 & 0.970020837841386 \tabularnewline
21 & 0.0133533869833497 & 0.0267067739666994 & 0.98664661301665 \tabularnewline
22 & 0.00701456581846378 & 0.0140291316369276 & 0.992985434181536 \tabularnewline
23 & 0.00484056407716548 & 0.00968112815433097 & 0.995159435922835 \tabularnewline
24 & 0.00207785044018236 & 0.00415570088036472 & 0.997922149559818 \tabularnewline
25 & 0.00101684487330364 & 0.00203368974660729 & 0.998983155126696 \tabularnewline
26 & 0.00221665562416331 & 0.00443331124832662 & 0.997783344375837 \tabularnewline
27 & 0.00101216483087423 & 0.00202432966174845 & 0.998987835169126 \tabularnewline
28 & 0.000445258345965295 & 0.000890516691930589 & 0.999554741654035 \tabularnewline
29 & 0.000625420746897083 & 0.00125084149379417 & 0.999374579253103 \tabularnewline
30 & 0.000608740510950594 & 0.00121748102190119 & 0.999391259489049 \tabularnewline
31 & 0.000274431009170536 & 0.000548862018341072 & 0.999725568990829 \tabularnewline
32 & 0.000151481598585461 & 0.000302963197170922 & 0.999848518401415 \tabularnewline
33 & 6.91038568810129e-05 & 0.000138207713762026 & 0.999930896143119 \tabularnewline
34 & 6.96353220422635e-05 & 0.000139270644084527 & 0.999930364677958 \tabularnewline
35 & 3.94892344527214e-05 & 7.89784689054428e-05 & 0.999960510765547 \tabularnewline
36 & 2.53073627633501e-05 & 5.06147255267003e-05 & 0.999974692637237 \tabularnewline
37 & 1.11827926846848e-05 & 2.23655853693695e-05 & 0.999988817207315 \tabularnewline
38 & 3.01734527774741e-05 & 6.03469055549482e-05 & 0.999969826547223 \tabularnewline
39 & 0.000155007246547871 & 0.000310014493095742 & 0.999844992753452 \tabularnewline
40 & 0.000148757788747276 & 0.000297515577494552 & 0.999851242211253 \tabularnewline
41 & 9.56311511331242e-05 & 0.000191262302266248 & 0.999904368848867 \tabularnewline
42 & 5.12898003509668e-05 & 0.000102579600701934 & 0.999948710199649 \tabularnewline
43 & 4.53062056899762e-05 & 9.06124113799524e-05 & 0.99995469379431 \tabularnewline
44 & 6.20522679736367e-05 & 0.000124104535947273 & 0.999937947732026 \tabularnewline
45 & 4.83152214827383e-05 & 9.66304429654767e-05 & 0.999951684778517 \tabularnewline
46 & 0.000107992062025476 & 0.000215984124050952 & 0.999892007937975 \tabularnewline
47 & 0.000123974732307476 & 0.000247949464614952 & 0.999876025267692 \tabularnewline
48 & 0.000127309747564096 & 0.000254619495128192 & 0.999872690252436 \tabularnewline
49 & 6.01245443029656e-05 & 0.000120249088605931 & 0.999939875455697 \tabularnewline
50 & 0.000278497986987704 & 0.000556995973975407 & 0.999721502013012 \tabularnewline
51 & 0.000423221069200944 & 0.000846442138401887 & 0.999576778930799 \tabularnewline
52 & 0.000349720384227133 & 0.000699440768454265 & 0.999650279615773 \tabularnewline
53 & 0.00029669023575237 & 0.00059338047150474 & 0.999703309764248 \tabularnewline
54 & 0.0002913777386722 & 0.000582755477344401 & 0.999708622261328 \tabularnewline
55 & 0.000341671034443956 & 0.000683342068887911 & 0.999658328965556 \tabularnewline
56 & 0.000941065138844269 & 0.00188213027768854 & 0.999058934861156 \tabularnewline
57 & 0.00114051657492755 & 0.00228103314985511 & 0.998859483425072 \tabularnewline
58 & 0.0027116089103381 & 0.00542321782067621 & 0.997288391089662 \tabularnewline
59 & 0.00214586794211133 & 0.00429173588422267 & 0.997854132057889 \tabularnewline
60 & 0.0331177386079024 & 0.0662354772158048 & 0.966882261392098 \tabularnewline
61 & 0.0233098523473471 & 0.0466197046946941 & 0.976690147652653 \tabularnewline
62 & 0.137169464450742 & 0.274338928901484 & 0.862830535549258 \tabularnewline
63 & 0.117627728124811 & 0.235255456249622 & 0.882372271875189 \tabularnewline
64 & 0.0971605463790853 & 0.194321092758171 & 0.902839453620915 \tabularnewline
65 & 0.198326472298045 & 0.396652944596089 & 0.801673527701956 \tabularnewline
66 & 0.293883351101208 & 0.587766702202416 & 0.706116648898792 \tabularnewline
67 & 0.189049576642398 & 0.378099153284796 & 0.810950423357602 \tabularnewline
68 & 0.393802690383803 & 0.787605380767606 & 0.606197309616197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147364&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.0031467803216849[/C][C]0.0062935606433698[/C][C]0.996853219678315[/C][/ROW]
[ROW][C]17[/C][C]0.164485356951793[/C][C]0.328970713903587[/C][C]0.835514643048207[/C][/ROW]
[ROW][C]18[/C][C]0.082548359084444[/C][C]0.165096718168888[/C][C]0.917451640915556[/C][/ROW]
[ROW][C]19[/C][C]0.0397487351735584[/C][C]0.0794974703471167[/C][C]0.960251264826442[/C][/ROW]
[ROW][C]20[/C][C]0.0299791621586144[/C][C]0.0599583243172289[/C][C]0.970020837841386[/C][/ROW]
[ROW][C]21[/C][C]0.0133533869833497[/C][C]0.0267067739666994[/C][C]0.98664661301665[/C][/ROW]
[ROW][C]22[/C][C]0.00701456581846378[/C][C]0.0140291316369276[/C][C]0.992985434181536[/C][/ROW]
[ROW][C]23[/C][C]0.00484056407716548[/C][C]0.00968112815433097[/C][C]0.995159435922835[/C][/ROW]
[ROW][C]24[/C][C]0.00207785044018236[/C][C]0.00415570088036472[/C][C]0.997922149559818[/C][/ROW]
[ROW][C]25[/C][C]0.00101684487330364[/C][C]0.00203368974660729[/C][C]0.998983155126696[/C][/ROW]
[ROW][C]26[/C][C]0.00221665562416331[/C][C]0.00443331124832662[/C][C]0.997783344375837[/C][/ROW]
[ROW][C]27[/C][C]0.00101216483087423[/C][C]0.00202432966174845[/C][C]0.998987835169126[/C][/ROW]
[ROW][C]28[/C][C]0.000445258345965295[/C][C]0.000890516691930589[/C][C]0.999554741654035[/C][/ROW]
[ROW][C]29[/C][C]0.000625420746897083[/C][C]0.00125084149379417[/C][C]0.999374579253103[/C][/ROW]
[ROW][C]30[/C][C]0.000608740510950594[/C][C]0.00121748102190119[/C][C]0.999391259489049[/C][/ROW]
[ROW][C]31[/C][C]0.000274431009170536[/C][C]0.000548862018341072[/C][C]0.999725568990829[/C][/ROW]
[ROW][C]32[/C][C]0.000151481598585461[/C][C]0.000302963197170922[/C][C]0.999848518401415[/C][/ROW]
[ROW][C]33[/C][C]6.91038568810129e-05[/C][C]0.000138207713762026[/C][C]0.999930896143119[/C][/ROW]
[ROW][C]34[/C][C]6.96353220422635e-05[/C][C]0.000139270644084527[/C][C]0.999930364677958[/C][/ROW]
[ROW][C]35[/C][C]3.94892344527214e-05[/C][C]7.89784689054428e-05[/C][C]0.999960510765547[/C][/ROW]
[ROW][C]36[/C][C]2.53073627633501e-05[/C][C]5.06147255267003e-05[/C][C]0.999974692637237[/C][/ROW]
[ROW][C]37[/C][C]1.11827926846848e-05[/C][C]2.23655853693695e-05[/C][C]0.999988817207315[/C][/ROW]
[ROW][C]38[/C][C]3.01734527774741e-05[/C][C]6.03469055549482e-05[/C][C]0.999969826547223[/C][/ROW]
[ROW][C]39[/C][C]0.000155007246547871[/C][C]0.000310014493095742[/C][C]0.999844992753452[/C][/ROW]
[ROW][C]40[/C][C]0.000148757788747276[/C][C]0.000297515577494552[/C][C]0.999851242211253[/C][/ROW]
[ROW][C]41[/C][C]9.56311511331242e-05[/C][C]0.000191262302266248[/C][C]0.999904368848867[/C][/ROW]
[ROW][C]42[/C][C]5.12898003509668e-05[/C][C]0.000102579600701934[/C][C]0.999948710199649[/C][/ROW]
[ROW][C]43[/C][C]4.53062056899762e-05[/C][C]9.06124113799524e-05[/C][C]0.99995469379431[/C][/ROW]
[ROW][C]44[/C][C]6.20522679736367e-05[/C][C]0.000124104535947273[/C][C]0.999937947732026[/C][/ROW]
[ROW][C]45[/C][C]4.83152214827383e-05[/C][C]9.66304429654767e-05[/C][C]0.999951684778517[/C][/ROW]
[ROW][C]46[/C][C]0.000107992062025476[/C][C]0.000215984124050952[/C][C]0.999892007937975[/C][/ROW]
[ROW][C]47[/C][C]0.000123974732307476[/C][C]0.000247949464614952[/C][C]0.999876025267692[/C][/ROW]
[ROW][C]48[/C][C]0.000127309747564096[/C][C]0.000254619495128192[/C][C]0.999872690252436[/C][/ROW]
[ROW][C]49[/C][C]6.01245443029656e-05[/C][C]0.000120249088605931[/C][C]0.999939875455697[/C][/ROW]
[ROW][C]50[/C][C]0.000278497986987704[/C][C]0.000556995973975407[/C][C]0.999721502013012[/C][/ROW]
[ROW][C]51[/C][C]0.000423221069200944[/C][C]0.000846442138401887[/C][C]0.999576778930799[/C][/ROW]
[ROW][C]52[/C][C]0.000349720384227133[/C][C]0.000699440768454265[/C][C]0.999650279615773[/C][/ROW]
[ROW][C]53[/C][C]0.00029669023575237[/C][C]0.00059338047150474[/C][C]0.999703309764248[/C][/ROW]
[ROW][C]54[/C][C]0.0002913777386722[/C][C]0.000582755477344401[/C][C]0.999708622261328[/C][/ROW]
[ROW][C]55[/C][C]0.000341671034443956[/C][C]0.000683342068887911[/C][C]0.999658328965556[/C][/ROW]
[ROW][C]56[/C][C]0.000941065138844269[/C][C]0.00188213027768854[/C][C]0.999058934861156[/C][/ROW]
[ROW][C]57[/C][C]0.00114051657492755[/C][C]0.00228103314985511[/C][C]0.998859483425072[/C][/ROW]
[ROW][C]58[/C][C]0.0027116089103381[/C][C]0.00542321782067621[/C][C]0.997288391089662[/C][/ROW]
[ROW][C]59[/C][C]0.00214586794211133[/C][C]0.00429173588422267[/C][C]0.997854132057889[/C][/ROW]
[ROW][C]60[/C][C]0.0331177386079024[/C][C]0.0662354772158048[/C][C]0.966882261392098[/C][/ROW]
[ROW][C]61[/C][C]0.0233098523473471[/C][C]0.0466197046946941[/C][C]0.976690147652653[/C][/ROW]
[ROW][C]62[/C][C]0.137169464450742[/C][C]0.274338928901484[/C][C]0.862830535549258[/C][/ROW]
[ROW][C]63[/C][C]0.117627728124811[/C][C]0.235255456249622[/C][C]0.882372271875189[/C][/ROW]
[ROW][C]64[/C][C]0.0971605463790853[/C][C]0.194321092758171[/C][C]0.902839453620915[/C][/ROW]
[ROW][C]65[/C][C]0.198326472298045[/C][C]0.396652944596089[/C][C]0.801673527701956[/C][/ROW]
[ROW][C]66[/C][C]0.293883351101208[/C][C]0.587766702202416[/C][C]0.706116648898792[/C][/ROW]
[ROW][C]67[/C][C]0.189049576642398[/C][C]0.378099153284796[/C][C]0.810950423357602[/C][/ROW]
[ROW][C]68[/C][C]0.393802690383803[/C][C]0.787605380767606[/C][C]0.606197309616197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147364&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147364&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.00314678032168490.00629356064336980.996853219678315
170.1644853569517930.3289707139035870.835514643048207
180.0825483590844440.1650967181688880.917451640915556
190.03974873517355840.07949747034711670.960251264826442
200.02997916215861440.05995832431722890.970020837841386
210.01335338698334970.02670677396669940.98664661301665
220.007014565818463780.01402913163692760.992985434181536
230.004840564077165480.009681128154330970.995159435922835
240.002077850440182360.004155700880364720.997922149559818
250.001016844873303640.002033689746607290.998983155126696
260.002216655624163310.004433311248326620.997783344375837
270.001012164830874230.002024329661748450.998987835169126
280.0004452583459652950.0008905166919305890.999554741654035
290.0006254207468970830.001250841493794170.999374579253103
300.0006087405109505940.001217481021901190.999391259489049
310.0002744310091705360.0005488620183410720.999725568990829
320.0001514815985854610.0003029631971709220.999848518401415
336.91038568810129e-050.0001382077137620260.999930896143119
346.96353220422635e-050.0001392706440845270.999930364677958
353.94892344527214e-057.89784689054428e-050.999960510765547
362.53073627633501e-055.06147255267003e-050.999974692637237
371.11827926846848e-052.23655853693695e-050.999988817207315
383.01734527774741e-056.03469055549482e-050.999969826547223
390.0001550072465478710.0003100144930957420.999844992753452
400.0001487577887472760.0002975155774945520.999851242211253
419.56311511331242e-050.0001912623022662480.999904368848867
425.12898003509668e-050.0001025796007019340.999948710199649
434.53062056899762e-059.06124113799524e-050.99995469379431
446.20522679736367e-050.0001241045359472730.999937947732026
454.83152214827383e-059.66304429654767e-050.999951684778517
460.0001079920620254760.0002159841240509520.999892007937975
470.0001239747323074760.0002479494646149520.999876025267692
480.0001273097475640960.0002546194951281920.999872690252436
496.01245443029656e-050.0001202490886059310.999939875455697
500.0002784979869877040.0005569959739754070.999721502013012
510.0004232210692009440.0008464421384018870.999576778930799
520.0003497203842271330.0006994407684542650.999650279615773
530.000296690235752370.000593380471504740.999703309764248
540.00029137773867220.0005827554773444010.999708622261328
550.0003416710344439560.0006833420688879110.999658328965556
560.0009410651388442690.001882130277688540.999058934861156
570.001140516574927550.002281033149855110.998859483425072
580.00271160891033810.005423217820676210.997288391089662
590.002145867942111330.004291735884222670.997854132057889
600.03311773860790240.06623547721580480.966882261392098
610.02330985234734710.04661970469469410.976690147652653
620.1371694644507420.2743389289014840.862830535549258
630.1176277281248110.2352554562496220.882372271875189
640.09716054637908530.1943210927581710.902839453620915
650.1983264722980450.3966529445960890.801673527701956
660.2938833511012080.5877667022024160.706116648898792
670.1890495766423980.3780991532847960.810950423357602
680.3938026903838030.7876053807676060.606197309616197







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level380.716981132075472NOK
5% type I error level410.773584905660377NOK
10% type I error level440.830188679245283NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 38 & 0.716981132075472 & NOK \tabularnewline
5% type I error level & 41 & 0.773584905660377 & NOK \tabularnewline
10% type I error level & 44 & 0.830188679245283 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147364&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]38[/C][C]0.716981132075472[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]41[/C][C]0.773584905660377[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.830188679245283[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147364&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147364&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level380.716981132075472NOK
5% type I error level410.773584905660377NOK
10% type I error level440.830188679245283NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}